import{s as oe,n as ce,o as ie}from"../chunks/scheduler.53228c21.js";import{S as de,i as ae,e as i,s as r,c as x,h as le,a as d,d as n,b as o,f as O,g as C,j as F,k as A,l,m as s,n as D,t as T,o as S,p as w}from"../chunks/index.100fac89.js";import{C as ue}from"../chunks/CopyLLMTxtMenu.2cdbb51f.js";import{D as Y}from"../chunks/Docstring.d46840f1.js";import{H as re,E as pe}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.3fcbbc76.js";function me(Z){let a,I,M,z,m,G,f,V,h,ee='This scheduler is a part of the <code>ConsistencyDecoderPipeline</code> and was introduced in <a href="https://openai.com/dall-e-3" rel="nofollow">DALL-E 3</a>.',k,g,te='The original codebase can be found at <a href="https://github.com/openai/consistency_models" rel="nofollow">openai/consistency_models</a>.',N,_,U,c,$,J,u,y,K,E,ne=`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.`,Q,p,b,X,L,se=`Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
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<p>A scaled input sample.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),b=new Y({props:{name:"step",anchor:"diffusers.schedulers.ConsistencyDecoderScheduler.step",parameters:[{name:"model_output",val:": Tensor"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"sample",val:": Tensor"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.schedulers.ConsistencyDecoderScheduler.step.model_output",description:`<strong>model_output</strong> (<code>torch.Tensor</code>) &#x2014;
The direct output from the learned diffusion model.`,name:"model_output"},{anchor:"diffusers.schedulers.ConsistencyDecoderScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code>) &#x2014;
The current timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.schedulers.ConsistencyDecoderScheduler.step.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
A current instance of a sample created by the diffusion process.`,name:"sample"},{anchor:"diffusers.schedulers.ConsistencyDecoderScheduler.step.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A random number generator.`,name:"generator"},{anchor:"diffusers.schedulers.ConsistencyDecoderScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a
<code>~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput</code> or <code>tuple</code>.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_consistency_decoder.py#L133",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If return_dict is <code>True</code>,
<code>~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput</code> is returned, otherwise
a tuple is returned where the first element is the sample tensor.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput</code> or <code>tuple</code></p>
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